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Lü Enli, Ruan Qingsong, Liu Yanhua, Wang Feiren, Luo Yizhi. Intelligent forklift obstacle detection based on dynamic change of laser scanning area[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(3): 67-74. DOI: 10.11975/j.issn.1002-6819.2019.03.009
Citation: Lü Enli, Ruan Qingsong, Liu Yanhua, Wang Feiren, Luo Yizhi. Intelligent forklift obstacle detection based on dynamic change of laser scanning area[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(3): 67-74. DOI: 10.11975/j.issn.1002-6819.2019.03.009

Intelligent forklift obstacle detection based on dynamic change of laser scanning area

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  • Received Date: September 01, 2018
  • Revised Date: January 06, 2019
  • Published Date: January 31, 2019
  • Abstract: Dried fruits should be stored in warehouse by placing and stacking on the shelves. By using intelligent forklift to store and take goods on the shelves, the warehouse efficiency could be effectively solved, and the warehouse management of dried fruits could be promoted to be standardized and intelligent. Obstacle detection is the primary guarantee for the safe operation of intelligent forklifts, and the detection effect is also related to the efficient operation of intelligent forklifts in warehouse, as a key technology of intelligent vehicles, it has gradually become a research hot topic. However, the current researches focuse on small multi-degree-freedom intelligent vehicles, there is no research on obstacles detection methods for large intelligent forklift in dried fruit warehouse. Considering the limitations of warehouse layout, the detection region of traditional detection methods were mostly fixed shape, that means that the safety distance was fixed, so it was more suitable when forklift going straight in an open space, on the contrary, in the dried fruit warehouse with limited channel width, especially when turning, there would be false alarm, which would easily cause the large intelligent forklift to misjudge the objects that could be bypassed into potential obstacles, thus causing the forklift to change the road or stop sharply. In order to solve the false detection and realize the obstacle dynamic detection for large intelligent forklift in dried fruit warehouse, taking the reversing process of intelligent forklift as an example, an obstacles dynamic detection method based on dynamic change of laser scanning area with the speed and steering angle of large intelligent forklift in dried fruit warehouse was proposed in this paper. The real-time position and direction information of forklift in the global Cartesian coordinate system of warehouse was obtained by using on-board laser sensor SICK-NAV350, combining with the motion geometry model of forklift, the horizontal laser ranging sensor (SICK-LMS111) and the inclined laser ranging sensor (SICK-TIM561) scanning the obstacle in 2 planes, forming a dynamic detection area changing with the speed and steering angle of forklift. The real vehicle test results showed that the proposed method without error checking, the error detection rate of the sector method was 50.00% and that of the rectangle method was 10.00% in the testing of horizontal scan ranging sensor, the error detection rate of the sector method was 30.77% and that of the rectangle method was 69.23% in the testing of tilted scan ranging sensor. With tilted scanning range sensor as the auxiliary and horizontal scanning range sensor as the main part, a dynamic obstacle detection area based on fusion of 2 planes was formed, the minimum height of obstacles could be detected was about 31 mm when the installation angle of sensor SICK- TIM561 was 25.60°, the height to ground was 2 000 mm and the detection region width was set to 2 183 mm. The proposed method effectively solved the false alarm of intelligent forklift when driving in the warehouse, and was more suitable for warehousing and transportation than the traditional detection method, and improved the mobility and safety of intelligent forklift in warehouse. The research can provide reference for obstacle detection of large warehouse intelligent transport vehicles.
  • [1]
    王琪. 新疆干果出口的主要障碍与优化路径[J]. 湖北职业技术学院学报,2017,20(4):81-84.Wang Qi. The main obstacles and optimization path of dried fruit export in Xinjiang[J]. Journal of Hubei Polytechnic Institute, 2017, 20(4): 81-84. (in Chinese with English abstract)
    [2]
    Lang A, Gunthner W A. Evaluation of the usage of support vector machines for people detection for a collision warning system on a forklift[C]//International Conference on Hci in Business, Canada: Springer Verlag, 2017: 322-337.
    [3]
    Wenjie S, Yi Y, Mengyin F, et al. Real-time obstacles detection and status classification for collision warning in a vehicle active safety system[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(3): 758-773.
    [4]
    Rozsa Z, Sziranyi T. Obstacle prediction for automated guided vehicles based on point clouds measured by a tilted lidar sensor[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(8): 2708-2720.
    [5]
    金湘亮,曾云,陈迪平. 红外线测距系统的建立及其在汽车防撞系统中的应用[J]. 红外技术. 2001,23(3):43-45.Jin Xiangliang, Zeng Yun, Chen Diping. The establishment of infrared ranging system and it's application in vehicle collision avoidance system[J]. Infrared Technology. 2001, 23(3): 43-45. (in Chinese with English abstract)
    [6]
    王松德,韩运侠,朱小龙,等. 近红外传感器在汽车改造技术中的应用研究[J]. 光谱学与光谱分析. 2005,25(7):1061-1063.Wang Songde, Han Yunxia, Zhu Xiaolong, et al. Application of near infrared sensor to the technology of automobile transformation[J]. Spectroscopy and Spectral Analysis . 2005, 25(7): 1061-1063. (in Chinese with English abstract)
    [7]
    仇成群,胡天云. 基于超声波的汽车防撞报警系统的设计[J]. 制造业自动化. 2009,31(4):75-77.Qiu Chengqun, Hu Tianyun. The vehicle collision avoidance alarm apparatus system based on ultrasonic[J]. Manufacturing Automation. 2009, 31(4): 75-77. (in Chinese with English abstract)
    [8]
    张莹,张进,刘天飞. 超声波倒车防撞系统[J]. 通信技术. 2011,44(2):130-132.Zhang Ying, Zhang Jin, Liu Tianfei. Ultrasonic back-draft anti-collision system[J]. Communications Technology. 2011, 44(2): 130-132. (in Chinese with English abstract)
    [9]
    裴晓飞,刘昭度,马国成,等. 汽车主动避撞系统的安全距离模型和目标检测算法[J]. 汽车安全与节能学报,2012,3(1):26-33.Pei Xiaofei, Liu Zhaodu, Ma Guocheng, et al. Safe distance model and obstacle detection algorithms for a collision warning and collision avoidance system[J]. Journal of Automotive Safety and Energy. 2012, 3(1): 26-33. (in Chinese with English abstract)
    [10]
    张昱,宋骊平,虎小龙. 基于概率假设密度的汽车防撞雷达多目标跟踪[J]. 现代雷达. 2014,36(6):82-87.Zhang Yu, Song Liping, Hu Xiaolong. Multiple targets tracking of vehicle anti-collision radar based on probability hypothesis density[J]. Modern Radar. 2014, 36(6): 82-87. (in Chinese with English abstract)
    [11]
    Gohara R, Premachandra C, Kato K. A study on smooth automatic vehicle stopping control for suddenly-appeared obstacles[C]//International Conference on Vehicular Electronics and Safety. IEEE, 2015: 86-90.
    [12]
    王铮,赵晓,佘宏杰,等. 基于双目视觉的AGV障碍物检测与避障[J]. 计算机集成制造系统. 2018,24(2):400-409.Wang Zheng, Zhao Xiao, She Hongjie, et al. Obstacle detection and obstacle avoidance of AGV based on binocular vision[J]. Computer Integrated Manufacturing Systems. 2018, 24(2): 400-409. (in Chinese with English abstract)
    [13]
    常凤筠,崔旭东. 基于激光测距传感器的汽车防撞报警器的设计[J]. 应用激光. 2007,27(1):45-46.Chang Fengjun, Cui Xudong. The design of the vehicle anti-collision alarm apparatus based on distance laser sensor[J]. Applied Laser. 2007, 27(1): 45-46. (in Chinese with English abstract)
    [14]
    Kawarazaki N, Kuwae L T, Yoshidome T. Development of human following mobile robot system using laser range scanner[J]. Procedia Computer Science. 2015, 76: 455-460.
    [15]
    邹斌,谭亮,侯献军. 基于激光雷达的道路可行区域检测[J]. 武汉理工大学学报:交通科学与工程版. 2017,41(2):203-207.Zou Bin, Tan Liang, Hou Xianjun. Drivable road regions detection based on lidar[J]. Journal of Wuhan University of Technology: Transportation Science and Engineering. 2017, 41(2): 203-207. (in Chinese with English abstract)
    [16]
    Alajlan A M, Almasri M M, Elleithy K M. Multi-sensor based collision avoidance algorithm for mobile robot[C]// IEEE Long Island Systems, Applications and Technology Conference. IEEE, 2015: 1-6.
    [17]
    Almasri M M, Alajlan A M, Elleithy K M. Trajectory planning and collision avoidance algorithm for mobile robotics system[J]. IEEE Sensors Journal. 2016, 16(12): 5021-5028.
    [18]
    Mukhtar A, Xia L, Tang T B. Vehicle detection techniques for collision avoidance systems: A review[J]. IEEE Transactions on Intelligent Transportation Systems. 2015, 16(5): 2318-2338.
    [19]
    何勇,蒋浩,方慧,等. 车辆智能障碍物检测方法及其农业应用研究进展[J]. 农业工程学报. 2018,34(9):21-32.He Yong, Jiang Hao, Fang Hui, et al. Research progress of intelligent obstacle detection methods of vehicles and their application on agriculture[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE). 2018, 34(9): 21-32. (in Chinese with English abstract)
    [20]
    中国国家标准化管理委员会,中华人民共和国国家质量监督检验检疫总局. GB/T 6104-2005,机动工业车辆-术语[S].北京:中国标准出版社,2006.
    [21]
    曹昊天, 宋晓琳, 黄江. 基于弹性绳理论的自主车辆防碰撞的路径规划[J]. 汽车工程. 2014, 36(10): 1230-1236.Cao Haotian, Song Xiaolin, Huang Jiang. Path planning of autonomous vehicle for collision avoidance based on elastic band theory[J]. Automotive Engineering. 2014, 36(10): 1230-1236. (in Chinese with English abstract)
    [22]
    彭理群,吴超仲,黄珍,等. 考虑驾驶意图与动态环境的汽车避碰路径规划[J]. 交通运输系统工程与信息. 2016, 16(6):81-87.Peng Liqun, Wu Chaozhong, Huang Zhen, et al. Collision avoidance path planning with consideration of driver intention and dynamic traffic situation[J]. Journal of Transportation Systems Engineering and Information Technology. 2016, 16(6): 81-87. (in Chinese with English abstract)
    [23]
    中华人民共和国工业和信息化部. JB/T 3300-2010,平衡重式叉车-整机试验方法[S]. 北京:中国标准出版社,2010.
    [24]
    胡静涛,高雷,白晓平,等. 农业机械自动导航技术研究进展[J]. 农业工程学报. 2015,31(10):1-10.Hu Jingtao, Gao Lei, Bai Xiaoping, et al. Review of research on automatic guidance of agricultural vehicles[J]. Transactions of the Chinese Society of Agricultural Engineering. 2015, 31(10): 1-10. (in Chinese with English abstract)
    [25]
    李朋. 汽车主动防撞系统控制模式的研究[D]. 南京:南京航空航天大学,2012.Li Peng. Research of the Control Mode of Automotive Active Collision Avoidance System[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2012. (in Chinese with English abstract)
    [26]
    徐豪. 汽车主动防撞预警系统规避控制研究[D]. 长春:吉林大学,2012.Xu Hao. Research on Active Control of Vehicle Anti-Collision Warning System[D]. Changchun: Jilin University, 2012. (in Chinese with English abstract)
    [27]
    李红. 自动泊车系统路径规划与跟踪控制研究[D]. 长沙: 湖南大学,2014.Li Hong. A Study on Path Planning and Tracking Control Method for Automatic Parking System[D]. Changsha: Hunan University, 2014. (in Chinese with English abstract)
    [28]
    唐阳山,夏道华. 驾驶员对汽车防撞安全距离检测仿真研究[J]. 计算机仿真. 2016,33(7):449-453.Tang Yangshan, Xia Daohua. Driver to simulation study of automobile anti-collision safety distance detection[J]. Computer Simulation. 2016, 33(7): 449-453. (in Chinese with English abstract)
    [29]
    Clotet E, Martinez D, Moreno J, et al. Assistant personal robot (APR): Conception and application of a tele-operated assisted living robot[J]. Sensors. 2016, 16(5): 610-633.
    [30]
    Matthies L, Grandjean P. Stochastic performance modeling and evaluation of obstacle detectability with imaging range sensors[J]. IEEE Transactions on Robotics and Automation. 1994, 10(6): 783-792.
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